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Positive and Negative Emotion Classification Based on Multi-Channel ORIGINAL RESEARCH published: 26 August 2021 doi: 10.3389/fnbeh.2021.720451 Positive and Negative Emotion Classification Based on Multi-channel Fangfang Long 1†, Shanguang Zhao 2†, Xin Wei 3,4*, Siew-Cheok Ng 5, Xiaoli Ni 3, Aiping Chi 6, Peng Fang 7*, Weigang Zeng 4 and Bokun Wei 8 1Department of Psychology, Nanjing University, Nanjing, China, 2Centre for Sport and Exercise Sciences, University of Malaya, Kuala Lumpur, Malaysia, 3Institute of Social Psychology, School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an, China, 4Key & Core Technology Innovation Institute of the Greater Bay Area, Guangdong, China, 5Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia, 6School of Sports, Shaanxi Normal University, Xi’an, China, 7Department of the Psychology of Military Medicine, Air Force Medical University, Xi’an, China, 8Xi’an Middle School of Shaanxi Province, Xi’an, China The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the Edited by: emotional video evoked method. The results show that the energy ratio and differential Eva M. Marco, entropy of the frequency band can be used to classify positive and negative emotions Complutense University of Madrid, Spain effectively, and the best effect can be achieved by using an SVM classifier. When only the Reviewed by: forehead and forehead signals are used, the highest classification accuracy can reach Vittorio B. Lippi, 66%. When the data of all channels are used, the highest accuracy of the model can University of Freiburg Medical Center, reach 82%. After channel selection, the best model of this study can be obtained. The Germany Fatima Megala Nathan, accuracy is more than 86%. Yale-NUS College, Singapore Keywords: EEG, emotion classification, support vector machine, decision tree, back propagation neural network, *Correspondence: k-nearest neighbor Xin Wei [email protected] Peng Fang [email protected] INTRODUCTION † These authors have contributed Emotions play a critical role in everyday life, reflecting a person’s current physical and mental equally to this work state and significantly impacting cognition, communication, and decision-making. Emotions are Specialty section: generally considered to have two dimensions: arousal and valence, with arousal referring to the This article was submitted to Emotion Regulation and Processing, emotion’s intensity; valence referring to the specific emotional content, divided into positive, a section of the journal negative, and neutral feelings (Kim et al., 2013; Bailen et al., 2019). Positive emotions can enhance Frontiers in Behavioral Neuroscience subjective well-being and promote physical and mental health, while persistent negative emotions will affect people’s physical and mental health and work status (Gupta, 2019). Different emotions Received: 04 June 2021 arise in response to external environmental stimuli and are accompanied by changes in personal Accepted: 29 July 2021 representations and psychological reactions, measured and identified by scientific methods Published: 26 August 2021 (Wolf, 2015). Citation: Previous studies have shown that many signals enable us to identify emotions. The most intuitive Long F, Zhao S, Wei X, Ng S-C, Ni X, expression, voice, posture signals, EEG, ECG, EMG, breathing, and other physiological signals can Chi A, Fang P, Zeng W and Wei B also measure an emotion. Among many signals that can reflect emotional changes, EEG, with high (2021) Positive and Negative Emotion temporal resolution and non-artifactual characteristics, has been valued by many researchers and Classification Based on Multi-channel. is a standard method for emotion recognition. There are some features in EEG signals, which have Front. Behav. Neurosci. 15:720451. a strong ability of emotion classification. Petrantonakis and Hadjileontiadis(2010) proposed using doi: 10.3389/fnbeh.2021.720451 higher-order crossover features to extract EEG features, tested four different classifiers, and finally Frontiers in Behavioral Neuroscience| www.frontiersin.org 1 August 2021 | Volume 15 | Article 720451 Long et al. Positive and Negative Emotion Classification implemented a robust emotion classification method. Chen the classification effect? Which brain region will extract the et al.(2019) verified that the feature of differential entropy features to achieve the best classification effect? Third, there significantly affects sentiment classification. In the distribution is a tendency to use as few channels as possible to identify of brain regions related to emotion classification, there are some emotions for portability. One study found that when using only differences in the response of different brain regions to different the forehead electrode (Fp1 and Fp2) and using the gradient emotions. However, most studies on emotion classification lifting Decision Tree (DT) algorithm to classify happiness and were based on multi-channel EEG signals (Gonzalez et al., sadness, its accuracy can also reach 95.78% (Al-Nafjan et al., 2019; Goshvarpour and Goshvarpour, 2019). Many valuable 2017). However, no studies have been conducted to compare the achievements have been obtained regarding the differences and effect of dual and multi-channel classification simultaneously. Is functions of different brain regions in emotion classification. For the classification effect of two-channel still better than that of example, a study shows that the prefrontal lobe and occipital lobe multi-channel under the same experimental setting? greatly contribute to emotion classification (Asghar et al., 2019). Based on the above problems in emotion recognition research, Firpi and Vogelstein(2011), in a feature selection research work, this study focused on emotions consisting of validity and it is concluded that electrodes F3, F4, T8 have the best effect on arousal when classifying emotions and focused on positive emotion classification. and negative emotions within these two categories. The EEG Although there have been many studies on emotion signals were extracted when the subjects watched positive and recognition of EEG signals, several problems are still not clear negative videos, and four classifiers were selected to identify in previous studies. First of all, most of the available data sets emotion classification. In this study, Support Vector Machine for emotion recognition use image, video, audio, and other (SVM), DT, Back Propagation Neural Network (BPNN), and ways to induce emotional changes. Some researchers used EEG k-Nearest Neighbor (kNN) algorithms were used to examine signals from subjects’ happiness, anger, sadness, and joy videos the classification effect of energy share and differential entropy to classify these four types of emotions in related studies (Calix features, the classification effect of differential entropy features et al., 2012). Other studies classify data based on emotions such in different frequency bands, and the classification effect as happiness, relaxation, grief, and fear (Alazrai et al., 2018). of differential entropy features in different brain regions. Therefore, the current criteria for emotion classification are Meanwhile, in the analysis channel, the emotion recognition varied due to the high complexity and abstractness of emotion is carried out on the multi-channel and prefrontal lobe itself. As a result, many researchers fail to reach a unified dual-channel data to explore whether there is a difference in the emotional classification standard when doing work related to classification accuracy. emotion recognition. In practice, some studies directly use the label of evoked data as the label of the final emotion classification, MATERIALS AND METHODS but it may exist under the negative emotion but does not induce the negative emotion. Therefore, there is confusion in the sample Participants classification at the beginning. Secondly, in the extraction of Twenty-six participants (age range to 18–20, M = 19, SD = 0.48; EEG signals, the previous research found the most relevant 50% female) were recruited through flyers. The subjects had features of emotion from EEG signals. At present, four kinds standard visual or corrected visual acuity, normal hearing, and of features have been used for the emotion classification of no significant emotional problems or psychiatric disorders by EEG signals: time-domain features, frequency domain features, the State-Trait Anxiety Inventory (STAI) and Beck Depression statistical features, and time-frequency domain features (Torres Inventory (BDI). No coffee or alcoholic beverages were et al., 2020). Although many of the above features have been consumed within 24 h before the start of the experiment. All reported, there is still no detailed research to show which EEG subjects signed an informed consent form and received some signal feature combinations are most significantly related to remuneration at the end of the experiment. If the subjects did emotion classification. Recently, researchers have proposed that not accept the film’s content during the experiment, they could differential entropy has a good classification effect in emotion choose not to watch the film or terminate the experiment. The classification. The characteristics of energy proportion and study protocol was approved by the Ethics Committee of the Xian differential entropy have significant effects on the two-channel Jiaotong University. emotion classification (Al-Nafjan et al., 2017).
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